IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics最新文献

{"title":"Alternative Tests for the Selection of Model Variables","authors":"N. Mass, P. Senge","doi":"10.1109/TSMC.1978.4309998","DOIUrl":"https://doi.org/10.1109/TSMC.1978.4309998","url":null,"abstract":"This paper contrasts two approaches to testing the importance of model variables: single-equation statistical tests, such as are used in regression analysis, and model-behavior tests. The paper attempts to show that tests which analyze the impact of individual variables on model behavior are better suited, both theoretically and operationally, to the task of selecting model variables. Conversely, the analysis shows that statistical tests should not be viewed as tests of model specification per se, but as tests of a particular type of data usefulness: they warn the modeler when available data do not permit accurate estimation of a model parameter. However, as a detailed example illustrates, a model relationship may be difficult to estimate yet extremely important for overall behavior. The paper concludes by summarizing two recent applications of model-behavior testing to analyze alternative business-cycle theories and alternative models for capital investment.","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78932329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operations Research","authors":"K. Swarup, P. K. Gupta, M. Mohan, G. Nair","doi":"10.1109/tsmc.1980.4308489","DOIUrl":"https://doi.org/10.1109/tsmc.1980.4308489","url":null,"abstract":"OPRE 6250 Global Supply Chain Management (2 semester hours) Executive Education Course. This course addresses the design and management of global supply chain including international sourcing, integration of suppliers and distribution channels. Prerequisite: OPRE 62 01 or OPRE 6302 or consent of instructor. (2-0) Y OPRE 6271 Project Overview, Strategic and Process Management (2 semester hours) Introduces the project lifecycle, typical project management processes, leadership and teaming in project management, the relevance of business process analysis, strategic alignment of projects, and financial considerations in project selection. (2-0) R OPRE 6301 (SYSM 6303) Quantitative Introduction to Risk and Uncertainty in Business (3 semester hours) Introduction to statistical and probabilistic methods and theory applicable to situations faced by managers. Topics include: data presentation and summarization, regression analysis, fundamental probability theory and random variables, introductory decision analysis, estimation, confidence intervals, hypothesis testing, and One Way ANOVA (Some sections of this class may require a laptop computer). (3-0) S OPRE 6302 Operations Management (3 semester hours) Operations Management integrates all of the activities and processes that are necessary to provide products and services. This course overviews methods and models that help managers make better operating decisions over time. How these methods will allow firms to operate both manufacturing and service facilities in order to compete in a global environment will also be discussed. Prerequisite: OPRE 6301 (3-0) S OPRE 6303 Quantitative Foundations of Business (3 semester hours) This course discusses the applications of some basic mathematical concepts necessary for the business environment. Students are introduced to selected topics, including those in college algebra, matrix algebra, calculus, and optimization, and their usage in the context of managerial decision-making. MS Excel is used to illustrate and understand the core concepts. (3-0) S OPRE 6311 Game Theory (3 semester hours) Two person zero-sum and nonzero-sum games; Nash equilibrium; use of LP and Complementarity, N-person games; core, nucleolus, stable sets, etc. Applications to market equilibrium problems. (3-0) R OPRE 6325 (HMGT 6325) Healthcare Operations Management (3 semester hours) Explores how effectively managing and continuously improving the end-to-end heal care supply chain provides a competitive advantage. Topics include supply chain fundamentals, key players in the health care supply chain and their challenges, how the health care supply chain works, impact of technology on supply chain performance, and lean six sigma methodology. Simulations and case studies will reinforce the learning. (3-0) T OPRE 6332 Spreadsheet Modeling and Analytics (3 semester hours) This course explains the concepts of effective spreadsheet design and model building utilizing the electronic spreadsheet as the ","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79984748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate multilinear regression.","authors":"Ya Su,&nbsp;Xinbo Gao,&nbsp;Xuelong Li,&nbsp;Dacheng Tao","doi":"10.1109/TSMCB.2012.2195171","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2195171","url":null,"abstract":"<p><p>Conventional regression methods, such as multivariate linear regression (MLR) and its extension principal component regression (PCR), deal well with the situations that the data are of the form of low-dimensional vector. When the dimension grows higher, it leads to the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. However, little attention has been paid to such a problem. This paper first adopts an in-depth investigation to the USP in PCR, which answers three questions: 1) Why is USP produced? 2) What is the condition for USP, and 3) How is the influence of USP on regression. With the help of the above analysis, the principal components selection problem of PCR is presented. Subsequently, to address the problem of PCR, a multivariate multilinear regression (MMR) model is proposed which gives a substitutive solution to MLR, under the condition of multilinear objects. The basic idea of MMR is to transfer the multilinear structure of objects into the regression coefficients as a constraint. As a result, the regression problem is reduced to find two low-dimensional coefficients so that the principal components selection problem is avoided. Moreover, the sample size needed for solving MMR is greatly reduced so that USP is alleviated. As there is no closed-form solution for MMR, an alternative projection procedure is designed to obtain the regression matrices. For the sake of completeness, the analysis of computational cost and the proof of convergence are studied subsequently. Furthermore, MMR is applied to model the fitting procedure in the active appearance model (AAM). Experiments are conducted on both the carefully designed synthesizing data set and AAM fitting databases verified the theoretical analysis.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2195171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30674224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.","authors":"Michael J Fu,&nbsp;M Cenk Cavuşoğlu","doi":"10.1109/TSMCB.2012.2197387","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2197387","url":null,"abstract":"<p><p>Haptic interface research benefits from accurate human arm models for control and system design. The literature contains many human arm dynamic models but lacks detailed variability analyses. Without accurate measurements, variability is modeled in a very conservative manner, leading to less than optimal controller and system designs. This paper not only presents models for human arm dynamics but also develops inter- and intrasubject variability models for a stylus-based haptic device. Data from 15 human subjects (nine male, six female, ages 20-32) were collected using a Phantom Premium 1.5a haptic device for system identification. In this paper, grip-force-dependent models were identified for 1-3-N grip forces in the three spatial axes. Also, variability due to human subjects and grip-force variation were modeled as both structured and unstructured uncertainties. For both forms of variability, the maximum variation, 95 %, and 67 % confidence interval limits were examined. All models were in the frequency domain with force as input and position as output. The identified models enable precise controllers targeted to a subset of possible human operator dynamics.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2197387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30686407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems.","authors":"Qi Zhou,&nbsp;Peng Shi,&nbsp;Honghai Liu,&nbsp;Shengyuan Xu","doi":"10.1109/TSMCB.2012.2196432","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2196432","url":null,"abstract":"<p><p>This paper focuses on the problem of neural-network-based decentralized adaptive output-feedback control for a class of nonlinear strict-feedback large-scale stochastic systems. The dynamic surface control technique is used to avoid the explosion of computational complexity in the backstepping design process. A novel direct adaptive neural network approximation method is proposed to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. It is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded in a mean square. Simulation results are provided to demonstrate the effectiveness of the developed control design approach.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2196432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30641268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature selection with harmony search.","authors":"Ren Diao,&nbsp;Qiang Shen","doi":"10.1109/TSMCB.2012.2193613","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2193613","url":null,"abstract":"<p><p>Many search strategies have been exploited for the task of feature selection (FS), in an effort to identify more compact and better quality subsets. Such work typically involves the use of greedy hill climbing (HC), or nature-inspired heuristics, in order to discover the optimal solution without going through exhaustive search. In this paper, a novel FS approach based on harmony search (HS) is presented. It is a general approach that can be used in conjunction with many subset evaluation techniques. The simplicity of HS is exploited to reduce the overall complexity of the search process. The proposed approach is able to escape from local solutions and identify multiple solutions owing to the stochastic nature of HS. Additional parameter control schemes are introduced to reduce the effort and impact of parameter configuration. These can be further combined with the iterative refinement strategy, tailored to enforce the discovery of quality subsets. The resulting approach is compared with those that rely on HC, genetic algorithms, and particle swarm optimization, accompanied by in-depth studies of the suggested improvements.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2193613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30653139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approximate optimal control design for nonlinear one-dimensional parabolic PDE systems using empirical eigenfunctions and neural network.","authors":"Biao Luo,&nbsp;Huai-Ning Wu","doi":"10.1109/TSMCB.2012.2194781","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2194781","url":null,"abstract":"<p><p>This paper addresses the approximate optimal control problem for a class of parabolic partial differential equation (PDE) systems with nonlinear spatial differential operators. An approximate optimal control design method is proposed on the basis of the empirical eigenfunctions (EEFs) and neural network (NN). First, based on the data collected from the PDE system, the Karhunen-Loève decomposition is used to compute the EEFs. With those EEFs, the PDE system is formulated as a high-order ordinary differential equation (ODE) system. To further reduce its dimension, the singular perturbation (SP) technique is employed to derive a reduced-order model (ROM), which can accurately describe the dominant dynamics of the PDE system. Second, the Hamilton-Jacobi-Bellman (HJB) method is applied to synthesize an optimal controller based on the ROM, where the closed-loop asymptotic stability of the high-order ODE system can be guaranteed by the SP theory. By dividing the optimal control law into two parts, the linear part is obtained by solving an algebraic Riccati equation, and a new type of HJB-like equation is derived for designing the nonlinear part. Third, a control update strategy based on successive approximation is proposed to solve the HJB-like equation, and its convergence is proved. Furthermore, an NN approach is used to approximate the cost function. Finally, we apply the developed approximate optimal control method to a diffusion-reaction process with a nonlinear spatial operator, and the simulation results illustrate its effectiveness.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2194781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30619746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linearithmic time sparse and convex maximum margin clustering.","authors":"Xiao-Lei Zhang,&nbsp;Ji Wu","doi":"10.1109/TSMCB.2012.2197824","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2197824","url":null,"abstract":"<p><p>Recently, a new clustering method called maximum margin clustering (MMC) was proposed and has shown promising performances. It was originally formulated as a difficult nonconvex integer problem. To make the MMC problem practical, the researchers either relaxed the original MMC problem to inefficient convex optimization problems or reformulated it to nonconvex optimization problems, which sacrifice the convexity for efficiency. However, no approaches can both hold the convexity and be efficient. In this paper, a new linearithmic time sparse and convex MMC algorithm, called support-vector-regression-based MMC (SVR-MMC), is proposed. Generally, it first uses the SVR as the core of the MMC. Then, it is relaxed as a convex optimization problem, which is iteratively solved by the cutting-plane algorithm. Each cutting-plane subproblem is further decomposed to a serial supervised SVR problem by a new global extended-level method (GELM). Finally, each supervised SVR problem is solved in a linear time complexity by a new sparse-kernel SVR (SKSVR) algorithm. We further extend the SVR-MMC algorithm to the multiple-kernel clustering (MKC) problem and the multiclass MMC (M3C) problem, which are denoted as SVR-MKC and SVR-M3C, respectively. One key point of the algorithms is the utilization of the SVR. It can prevent the MMC and its extensions meeting an integer matrix programming problem. Another key point is the new SKSVR. It provides a linear time interface to the nonlinear kernel scenarios, so that the SVR-MMC and its extensions can keep a linearthmic time complexity in nonlinear kernel scenarios. Our experimental results on various real-world data sets demonstrate the effectiveness and the efficiency of the SVR-MMC and its two extensions. Moreover, the unsupervised application of the SVR-MKC to the voice activity detection (VAD) shows that the SVR-MKC can achieve good performances that are close to its supervised counterpart, meet the real-time demand of the VAD, and need no labeling for model training.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2197824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30653140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint-structured-sparsity-based classification for multiple-measurement transient acoustic signals.","authors":"Haichao Zhang,&nbsp;Yanning Zhang,&nbsp;Nasser M Nasrabadi,&nbsp;Thomas S Huang","doi":"10.1109/TSMCB.2012.2196038","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2196038","url":null,"abstract":"<p><p>This paper investigates the joint-structured-sparsity-based methods for transient acoustic signal classification with multiple measurements. By joint structured sparsity, we not only use the sparsity prior for each measurement but we also exploit the structural information across the sparse representation vectors of multiple measurements. Several different sparse prior models are investigated in this paper to exploit the correlations among the multiple measurements with the notion of the joint structured sparsity for improving the classification accuracy. Specifically, we propose models with the joint structured sparsity under different assumptions: same sparse code model, common sparse pattern model, and a newly proposed joint dynamic sparse model. For the joint dynamic sparse model, we also develop an efficient greedy algorithm to solve it. Extensive experiments are carried out on real acoustic data sets, and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2196038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30634396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reverse control for humanoid robot task recognition.","authors":"Sovannara Hak,&nbsp;Nicolas Mansard,&nbsp;Olivier Stasse,&nbsp;Jean Paul Laumond","doi":"10.1109/TSMCB.2012.2193614","DOIUrl":"https://doi.org/10.1109/TSMCB.2012.2193614","url":null,"abstract":"<p><p>Efficient methods to perform motion recognition have been developed using statistical tools. Those methods rely on primitive learning in a suitable space, for example, the latent space of the joint angle and/or adequate task spaces. Learned primitives are often sequential: A motion is segmented according to the time axis. When working with a humanoid robot, a motion can be decomposed into parallel subtasks. For example, in a waiter scenario, the robot has to keep some plates horizontal with one of its arms while placing a plate on the table with its free hand. Recognition can thus not be limited to one task per consecutive segment of time. The method presented in this paper takes advantage of the knowledge of what tasks the robot is able to do and how the motion is generated from this set of known controllers, to perform a reverse engineering of an observed motion. This analysis is intended to recognize parallel tasks that have been used to generate a motion. The method relies on the task-function formalism and the projection operation into the null space of a task to decouple the controllers. The approach is successfully applied on a real robot to disambiguate motion in different scenarios where two motions look similar but have different purposes.</p>","PeriodicalId":55006,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TSMCB.2012.2193614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30590068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}